create app.py

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+import pdfplumber
+import spacy
+from sentence_transformers import SentenceTransformer, util
+
+# Load spaCy model and Sentence Transformer model
+nlp = spacy.load('en_core_web_md')
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def extract_text_from_pdf(pdf_path):
+    text = ''
+    with pdfplumber.open(pdf_path) as pdf:
+        for page in pdf.pages:
+            text += page.extract_text()
+    return text
+
+def extract_text_from_txt(txt_path):
+    with open(txt_path, 'r') as file:
+        return file.read()
+
+def analyze_resume(resume_file, job_description_file):
+    # Extract text from the PDF resume
+    resume_text = extract_text_from_pdf(resume_file.name)
+    
+    # Extract text from the job description text file
+    job_description = extract_text_from_txt(job_description_file.name)
+
+    # Process the text with spaCy
+    doc = nlp(resume_text)
+
+    # Extract named entities from the resume
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+    
+    # Get embeddings and compute similarity
+    resume_embedding = model.encode(resume_text)
+    job_description_embedding = model.encode(job_description)
+    similarity = util.pytorch_cos_sim(resume_embedding, job_description_embedding).item()
+
+    return entities, similarity, job_description
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=analyze_resume,
+    inputs=[
+        gr.File(label="Upload Resume (PDF)"),
+        gr.File(label="Upload Job Description (TXT)")
+    ],
+    outputs=[
+        gr.JSON(label="Extracted Entities"),
+        gr.Textbox(label="Resume and Job Description Similarity"),
+        gr.Textbox(label="Job Description Text", interactive=False)
+    ],
+    title="Resume and Job Description Analyzer",
+    description="Upload your PDF resume and a TXT job description to extract entities and calculate similarity."
+)
+
+# Launch the interface
+iface.launch()